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Computer Science > Human-Computer Interaction

arXiv:2304.03442 (cs)
[Submitted on 7 Apr 2023]

Title:Generative Agents: Interactive Simulacra of Human Behavior

Authors:Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith
Ringel Morris, Percy Liang, Michael S. Bernstein
Download a PDF of the paper titled Generative Agents: Interactive
Simulacra of Human Behavior, by Joon Sung Park and 5 other authors
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    Abstract: Believable proxies of human behavior can empower
    interactive applications ranging from immersive environments to
    rehearsal spaces for interpersonal communication to prototyping
    tools. In this paper, we introduce generative
    agents--computational software agents that simulate believable
    human behavior. Generative agents wake up, cook breakfast, and
    head to work; artists paint, while authors write; they form
    opinions, notice each other, and initiate conversations; they
    remember and reflect on days past as they plan the next day. To
    enable generative agents, we describe an architecture that
    extends a large language model to store a complete record of the
    agent's experiences using natural language, synthesize those
    memories over time into higher-level reflections, and retrieve
    them dynamically to plan behavior. We instantiate generative
    agents to populate an interactive sandbox environment inspired by
    The Sims, where end users can interact with a small town of
    twenty five agents using natural language. In an evaluation,
    these generative agents produce believable individual and
    emergent social behaviors: for example, starting with only a
    single user-specified notion that one agent wants to throw a
    Valentine's Day party, the agents autonomously spread invitations
    to the party over the next two days, make new acquaintances, ask
    each other out on dates to the party, and coordinate to show up
    for the party together at the right time. We demonstrate through
    ablation that the components of our agent
    architecture--observation, planning, and reflection--each
    contribute critically to the believability of agent behavior. By
    fusing large language models with computational, interactive
    agents, this work introduces architectural and interaction
    patterns for enabling believable simulations of human behavior.

Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2304.03442 [cs.HC]
          (or arXiv:2304.03442v1 [cs.HC] for this version)
          https://doi.org/10.48550/arXiv.2304.03442
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Joon Sung Park [view email]
[v1] Fri, 7 Apr 2023 01:55:19 UTC (34,149 KB)
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